Precipitation and its properties are the most common parameters in addition to temperature in many weather-related applications. The precipitation parameters of interest may include the precipitation state (e.g. whether it is precipitating or not), the precipitation type, the intensity of precipitation and/or the accumulated precipitation. Because precipitation events are in practice almost always very unevenly distributed in time and location, a single observation represents a short period time in a small geographical area. Consequently, for weather-related applications that require reliable and up-to-date information regarding characteristics of precipitation, instant and local observations are the most useful ones.
Precipitation can be determined as precipitation particles of at least predetermined size and/or at least predetermined intensity falling on an area of interest under observation. Since precipitation may have a significant impact on operation in several walks of life and areas of business, it is highly desirable to be able to use a sensor, a plurality of sensors or a network of sensors to automatically/autonomously detect the current weather conditions with respect to precipitation parameters of interest.
However, such automatic determination of the type of the precipitating particles has proved very difficult. As example, the National Weather Service (NWS) sensor specification defines a professional grade sensor in a document entitled “Specification for an enhanced precipitation identification (EPI) sensor for the automated surface observing system (ASOS)”, specification No.: NWSS100-2MT-2-1-SP1001, March 2010, downloadable as “Specification_for_EPI_Sensor_April_r4_0.pdf” for example at https://www.fbo.gov/index?s=opportunity&mode=form&id=08177248d35976db 4a5c226b3b7d44ee&tab=core&_cview=0 on the priority date of the present patent application). However, none of the sensors proposed within this framework was able to meet these requirements, and acquisition program was suspended, see for example the update dated 2011 Jun. 27 at https://www.itdashboard.gov/investment/evaluation-history/338 (as available on the priority date of the present patent application).
One way to characterize the type and intensity of precipitation particles is to apply a set of predetermined weather codes. An example in this regard is provided in the World Meteorological Organization code table 4680, which includes weather codes that are often used to report the precipitation detection, state of the precipitating particles and intensity of the phenomenon. This code table is an implementation of a similar table (4677) for human observers. The observer reports the phenomenon that best describes the weather at observation time that typically actually is a 10-20 minutes period before the report sending time.
Hence, the precipitation may be characterized e.g. whether it is precipitating or not (the precipitation state) and possibly further whether the precipitation is liquid precipitation (e.g. rain, drizzle) or solid precipitation (e.g. snow, hail stones). In some scenarios it might be desirable to alternative or additionally, distinguish possible mixed precipitation type (e.g. rain and snow) or freezing precipitation (e.g. freezing rain or freezing drizzle). Further characteristics may include characterization of the precipitation intensity and accumulated precipitation.
An example of a known technique for precipitation characterization is an impedance-based detector for detecting the precipitation state, i.e. the presence or absence of precipitation. Such precipitation detectors may be referred to as ON/OFF sensors/detectors, and they typically rely on measuring the electrical impedance changes between a pair of measurement electrodes. Controlled heating may be applied to keep the measurement surface warm in order to melt snow and also to enable an improved detection of cessation of the precipitation event. A typical precipitation detector detects precipitation and controls a relay accordingly. An example of such a precipitation sensor is described e.g. in the document entitled “Rain Sensor” by Telecontrolli S.p.A., available on the priority date of the present patent application at http://www.teimasina.com/tc/docs/Rain_sensor.pdf. An enhanced sensor may have a built-in thermometer enabling more options for the relay control logic. An example of such an enhanced sensor is described in the document entitled “The DS-8 Rain/Snow Sensor Controller” by Automated Systems Engineering (ASE), Colorado Springs, Colo., available on the priority date of the present patent application at http://www.goase.com/ds8.htm.
Another example of known technique for precipitation characterization is an acoustic precipitation sensor that is mainly applicable for measuring properties of rain. On the other hand, such an acoustic sensor is typically less useful for detecting e.g. snow or drizzle that result in acoustic signals that may be weak and obscure. Consequently, such acoustic sensor is unreliable in detecting presence of snow or drizzle, or even presence of low intensity rain. Moreover, an ambient noise component typically present in the acoustic sensor signal is likely to disturb the sensor operation as well. On the other hand, an acoustic sensor may be able to measure rain accumulation quite accurately, due to the fact that the lowest rain intensities have a minor impact on the accumulated rain amount. As an example of acoustic sensor, Vaisala RAINCAP® sensor together with Vaisala Weather Transmitter WXT520 contains an acoustic rain detector. This exemplifying acoustic sensor is described e.g. in the document entitled “Vaisala RAINCAP® Sensor Technology”, available on the priority date of the present application at httpJ/www.vaisala.com/Vaisala %20Documents/Technology %20Descriptions/R AINCAP_Technology.pdf.
A further example of a known technique is an optical precipitation sensor. Optical precipitation sensors measure scattering and/or attenuation of light from the precipitation particles in a sample volume. Particle sizes and falling speed are estimated from the data, which is subsequently used in determining the precipitation type. Slow falling particles are characterized as snow. These type of sensors are able to detect precipitation and measure the rain intensity relatively well, while they typically have difficulties to estimate accumulated precipitation for other types of precipitation (typically expressed as water content), because the precipitation type determination is not accurate and/or reliable. Additionally, other flying particles of similar size as typical precipitation particles, such as flower seed and insects, easily cause false rain reports. U.S. Pat. No. 4,613,938 describes an example of such a method, and e.g. Biral HSS VPF 750 sensor uses such a technique (more detailed information regarding this sensor is available on the priority date of the present patent application e.g. at http://www.biral.com/meteorological-sensors/visibility-and-presentweather/hss-vpf-750-visibility-present-and-past-weather-sensor).
An optical sensor with a capacitive rain detector may be applied to eliminate false detections caused by dry (non-precipitation) particles. As an example, Vaisala Present Weather Detector PWD52 uses this technique and is able to report a full set of precipitation parameters (more information regarding this sensor is available on the priority date of the present patent application e.g. at http://www.vaisala.com/Vaisala %20Documents/Brochures %20and%20Datash eets/PWD52-Datasheet-B211065EN-A-lores.pdf).
Further in known techniques, the precipitation type may be characterized by employing a combination of optical forward scatter measurement and a capacitive detector. The optical system measures primarily the size (volume) of the precipitating particles and the capacitive measurement estimates the corresponding water content (weight). Typically the volume/weight ratio for snow is in the order of ten times bigger than that of rain. This technique, however, has problems, when the intensity of precipitation varies a lot and/or fast or when the intensity is relatively low, because the capacitive measurement is typically provides slower response to changes in precipitation intensity than the optical measurement. This may lead to false reports of precipitation type and/or intensity. Moreover, known sensors implementing this technique are relatively bulky and also expensive. Such sensors typically also require heating the whole sensor structure in order to keep it operational in changing conditions, possibly leading to excessive power consumption.
Precipitation accumulation (amount) is often measured using a tipping bucket sensor, where one tip corresponds to 0.2 mm of accumulated rainfall. When this type of precipitation accumulation sensor is clean and well calibrated, it is accurate, does not consume a significant amount of operating power and is cheap to manufacture. The best precipitation accumulation sensors typically employ a weighing principle, i.e. determining the weight of the accumulated precipitation, to measure the accumulated amount of water/snowfall. This kind of measurement technique may make it difficult to keep the sensor accurate and operational in all weather conditions. Hence the precipitation accumulation sensors can be used to reliably measure also intensity of moderate and heavy precipitation events, especially rain events.
Yet further in known techniques, data captured by a weather radar is often used to visualize precipitation location and intensity. However, because of its physical limitations a weather radar is not able to measure precipitation from very low clouds. If the precipitating cloud is far away from the radar, the data may give wrong impression of the actual intensity and/or location of the precipitation hitting the ground. As a few examples of shortcomings of a weather radar based precipitation characterization, the precipitation may have evaporated before reaching the ground or light precipitation particles such as snow may drift up to 100 km before hitting the ground. Similar limitations apply also to the determination of the precipitation type from the radar signals. Consequently, a relatively dense network of precipitation type and intensity sensors is typically required to enhance the radar signal processing, especially with respect to processing the noise thresholds of the radar signals near ground.
In summary regarding known techniques, most known precipitation sensors have acceptable performance only in rain, while even reliable detection of snow events has proved difficult. While impedance-based sensors can have a reasonable accuracy of intensity measurement regardless of the precipitation type and intensity, for other types of indirect precipitation sensors reliable characterization of the precipitation type is necessary to determine the liquid water equivalent precipitation intensity.